Literature DB >> 27524504

Atlas-guided generation of pseudo-CT images for MRI-only and hybrid PET-MRI-guided radiotherapy treatment planning.

Hossein Arabi1, Nikolaos Koutsouvelis, Michel Rouzaud, Raymond Miralbell, Habib Zaidi.   

Abstract

Magnetic resonance imaging (MRI)-guided attenuation correction (AC) of positron emission tomography (PET) data and/or radiation therapy (RT) treatment planning is challenged by the lack of a direct link between MRI voxel intensities and electron density. Therefore, even if this is not a trivial task, a pseudo-computed tomography (CT) image must be predicted from MRI alone. In this work, we propose a two-step (segmentation and fusion) atlas-based algorithm focusing on bone tissue identification to create a pseudo-CT image from conventional MRI sequences and evaluate its performance against the conventional MRI segmentation technique and a recently proposed multi-atlas approach. The clinical studies consisted of pelvic CT, PET and MRI scans of 12 patients with loco-regionally advanced rectal disease. In the first step, bone segmentation of the target image is optimized through local weighted atlas voting. The obtained bone map is then used to assess the quality of deformed atlases to perform voxel-wise weighted atlas fusion. To evaluate the performance of the method, a leave-one-out cross-validation (LOOCV) scheme was devised to find optimal parameters for the model. Geometric evaluation of the produced pseudo-CT images and quantitative analysis of the accuracy of PET AC were performed. Moreover, a dosimetric evaluation of volumetric modulated arc therapy photon treatment plans calculated using the different pseudo-CT images was carried out and compared to those produced using CT images serving as references. The pseudo-CT images produced using the proposed method exhibit bone identification accuracy of 0.89 based on the Dice similarity metric compared to 0.75 achieved by the other atlas-based method. The superior bone extraction resulted in a mean standard uptake value bias of  -1.5  ±  5.0% (mean  ±  SD) in bony structures compared to  -19.9  ±  11.8% and  -8.1  ±  8.2% achieved by MRI segmentation-based (water-only) and atlas-guided AC. Dosimetric evaluation using dose volume histograms and the average difference between minimum/maximum absorbed doses revealed a mean error of less than 1% for the both target volumes and organs at risk. Two-dimensional (2D) gamma analysis of the isocenter dose distributions at 1%/1 mm criterion revealed pass rates of 91.40  ±  7.56%, 96.00  ±  4.11% and 97.67  ±  3.6% for MRI segmentation, atlas-guided and the proposed methods, respectively. The proposed method generates accurate pseudo-CT images from conventional Dixon MRI sequences with improved bone extraction accuracy. The approach is promising for potential use in PET AC and MRI-only or hybrid PET/MRI-guided RT treatment planning.

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Mesh:

Year:  2016        PMID: 27524504     DOI: 10.1088/0031-9155/61/17/6531

Source DB:  PubMed          Journal:  Phys Med Biol        ISSN: 0031-9155            Impact factor:   3.609


  14 in total

1.  Novel adversarial semantic structure deep learning for MRI-guided attenuation correction in brain PET/MRI.

Authors:  Hossein Arabi; Guodong Zeng; Guoyan Zheng; Habib Zaidi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2019-07-01       Impact factor: 9.236

Review 2.  Emerging role of MRI in radiation therapy.

Authors:  Hersh Chandarana; Hesheng Wang; R H N Tijssen; Indra J Das
Journal:  J Magn Reson Imaging       Date:  2018-09-08       Impact factor: 4.813

3.  Deep-JASC: joint attenuation and scatter correction in whole-body 18F-FDG PET using a deep residual network.

Authors:  Isaac Shiri; Hossein Arabi; Parham Geramifar; Ghasem Hajianfar; Pardis Ghafarian; Arman Rahmim; Mohammad Reza Ay; Habib Zaidi
Journal:  Eur J Nucl Med Mol Imaging       Date:  2020-05-15       Impact factor: 9.236

4.  Deep embedding convolutional neural network for synthesizing CT image from T1-Weighted MR image.

Authors:  Lei Xiang; Qian Wang; Dong Nie; Lichi Zhang; Xiyao Jin; Yu Qiao; Dinggang Shen
Journal:  Med Image Anal       Date:  2018-03-30       Impact factor: 8.545

5.  Multi-Conditional Constraint Generative Adversarial Network-Based MR Imaging from CT Scan Data.

Authors:  Mingjie Liu; Wei Zou; Wentao Wang; Cheng-Bin Jin; Junsheng Chen; Changhao Piao
Journal:  Sensors (Basel)       Date:  2022-05-26       Impact factor: 3.847

6.  MRI-guided attenuation correction in torso PET/MRI: Assessment of segmentation-, atlas-, and deep learning-based approaches in the presence of outliers.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Magn Reson Med       Date:  2021-09-04       Impact factor: 3.737

7.  Synthetic 4D-CT of the thorax for treatment plan adaptation on MR-guided radiotherapy systems.

Authors:  Joshua N Freedman; Hannah E Bainbridge; Simeon Nill; David J Collins; Marc Kachelrieß; Martin O Leach; Fiona McDonald; Uwe Oelfke; Andreas Wetscherek
Journal:  Phys Med Biol       Date:  2019-05-23       Impact factor: 3.609

8.  Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning.

Authors:  Ninon Burgos; Filipa Guerreiro; Jamie McClelland; Benoît Presles; Marc Modat; Simeon Nill; David Dearnaley; Nandita deSouza; Uwe Oelfke; Antje-Christin Knopf; Sébastien Ourselin; M Jorge Cardoso
Journal:  Phys Med Biol       Date:  2017-03-14       Impact factor: 3.609

Review 9.  Applications of artificial intelligence and deep learning in molecular imaging and radiotherapy.

Authors:  Hossein Arabi; Habib Zaidi
Journal:  Eur J Hybrid Imaging       Date:  2020-09-23

10.  Deep learning-guided joint attenuation and scatter correction in multitracer neuroimaging studies.

Authors:  Hossein Arabi; Karin Bortolin; Nathalie Ginovart; Valentina Garibotto; Habib Zaidi
Journal:  Hum Brain Mapp       Date:  2020-05-21       Impact factor: 5.038

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